Deep feature-based automatic classification of mammograms.
Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 6; pp. 1199 - 1212 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143136813&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143136813 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2020 vid: 58 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143136813 143136813 NLM32200453 10.1007/s11517-020-02150-8 NLM32200453 143136813 ppf: 1199 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep feature-based automatic classification of mammograms. aug: au: Arora, Ridhi Rai, Prateek Kumar Raman, Balasubramanian affil: Indian Institute of Technology Roorkee, Roorkee, India sug: subj: Diagnosis, Computer Assisted Methods Breast Neoplasms Mammography Methods Image Processing, Computer Assisted Methods Female Calcinosis Breast Diseases Algorithms Pharmacokinetics Mammography Classification Female ab: Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis (CADx) approaches have been developed. To improve upon the state of the art, we proposed a deep ensemble transfer learning and neural network classifier for automatic feature extraction and classification. In computer-assisted mammography, deep learning-based architectures are generally not trained on mammogram images directly. Instead, the images are pre-processed beforehand, and then they are adopted to be given as input to the ensemble model proposed. The robust features extracted from the ensemble model are optimized into a feature vector which are further classified using the neural network (nntraintool). The network was trained and tested to separate out benign and malignant tumors, thus achieving an accuracy of 0.88 with an area under curve (AUC) of 0.88. The attained results show that the proposed methodology is a promising and robust CADx system for breast cancer classification. Graphical Abstract Flow diagram of the proposed approach. Figure depicts the deep ensemble extracting the robust features with the final classification using neural networks. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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